Method for performing diagnosis of a camera system of a motor vehicle, camera system and motor vehicle

ABSTRACT

The invention relates to a method for performing a diagnosis of a camera system (2) of a motor vehicle (1) by: providing at least one image (BD) by means of a camera (3); detecting an object (6) in the image (BD) by means of an image processing device; providing sensor data (SD) by means of at least one sensor (7) of the motor vehicle (1), wherein the sensor data (SD) characterizes environmental conditions of the motor vehicle (1); first classifying the object (6) and herein associating the object (6) with a class (K1, K2, K3, K4) among several predetermined classes (K1, K2, K3, K4) depending on the environmental conditions, wherein the classes (K1, K2, K3, K4) differ from each other with respect to the environmental conditions; second classifying the at least one object (6) and herein associating the object (6) with one of the classes (K1, K2, K3, K4) based on the image (BD) and independently of the sensor data (SD) by a classification device (12) using a predetermined classification model (11); and comparing classification results of the first and the second classification and performing a diagnosis depending on the comparison.

The invention relates to a method for performing a diagnosis of a camerasystem of a motor vehicle. In addition, the invention relates to acamera system formed for performing such a method, as well as to a motorvehicle with such a camera system.

Camera systems for motor vehicles are already known from the prior art.Presently, the interest is directed to a camera system, by means ofwhich objects in the environment of the motor vehicle can be detected.To this, front cameras are in particular employed, which usually provideimages of an environmental region in front of the motor vehicle. Thissequence of images is processed by means of an electronic imageprocessing device, which detects target objects in the images. To this,the images are subjected to an object detection algorithm. Suchdetection algorithms are already prior art and are for example based onpattern recognition. In order to detect a target object, first,so-called characteristic points can be extracted from the image and thena target object can be identified based on these characteristic points.As an example, therein, the following algorithms can be mentioned:Adaboost and HOG-SVM.

If a target object is identified in an image of the camera, thus, thistarget object can also be tracked over the subsequent images of thesequence. Therein, the target object is detected in each image, whereinthe detection in the current image has to be associated with thedetection from the previous image. By tracking the target object, thecurrent position of the target object in the image frame and thus alsothe current relative position of the target object with respect to themotor vehicle are always known. Therein, for example, the Lucas-Kanademethod can be used as the tracking algorithm.

A mentioned camera system with a front camera can be used as thecollision warning system, by means of which the driver can be warned ofa risk of collision with the target object. Such a collision warningsystem can for example output warning signals in order to acousticallyand/or optically and/or haptically inform the driver about the detectedrisk of collision. Additionally or alternatively, the camera system canalso be used as an automatic brake assist system, which is adapted toperform automatic brake interventions of the motor vehicle due to thedetected risk of collision. As the measure of the current risk ofcollision, the so-called time to collision can for example be used, thatis a period of time, which is presumably needed by the motor vehicle toreach the target object. This time to collision can be calculated fromthe estimated distance of the target object as well as from the relativespeed.

A camera system for detecting objects is for example known from thedocument US 2012/0119894 A1. This camera system is formed forclassifying the detected objects and can associate each object with aclass or category among three possible classes. To this, data of a radarsensor is also used.

A method, in which images of a camera on the one hand and data of aradar sensor on the other hand are combined with each other, isfurthermore known from the document U.S. Pat. No. 8,081,209 B2. Based onthe data of the radar sensor, here, regions of interest are determined,in which the images are then processed and in which therefore theobjects are searched.

A method for classifying objects with the aid of a classifier is knownfrom the document U.S. Pat. No. 7,724,962 B2. The training of theclassifier is effected based on brightness values of the images, whereintest image data is for example divided in three categories with respectto the brightness.

A particular challenge in today's camera systems is in performing adiagnosis of the camera system and thus being able to check the camerasystem for the reliability of the object recognition. In particular, thediagnosis of the known object detection algorithms as well as of theclassification algorithms has proven problematic. If such algorithms areused in a camera system of a motor vehicle, thus, in the prior art,there is basically no longer any possibility of checking thesealgorithms for possible detection errors and/or classification errors.

It is the object of the invention to demonstrate a solution, how acamera system of a motor vehicle can be particularly reliably diagnosedand thus checked for its functionality.

According to the invention, this object is solved by a method, by acamera system as well as by a motor vehicle having the featuresaccording to the respective independent claims. Advantageousimplementations of the invention are the subject matter of the dependentclaims, of the description and of the figures.

A method according to the invention serves for performing a diagnosis ofa camera system of a motor vehicle. At least one image of anenvironmental region of the motor vehicle is provided by means of acamera of the camera system. An electronic image processing device ofthe camera system detects at least one object external to vehicle in theimage, in particular using a detection algorithm. Therein, basically,any detection algorithm can be used such that presently the detectionalgorithm is not elaborated in more detail. In addition, sensor data isprovided by means of at least one sensor of the motor vehicle inparticular separate from the camera, wherein the sensor datacharacterizes environmental conditions of the motor vehicle and thusdepends on the current environmental conditions. Then, a firstclassification of the at least one object is performed by associatingthe object with a class among several predetermined classes depending onthe environmental conditions. Therein, the classes differ from eachother with respect to the environmental conditions. Independently ofthat, a second classification of the at least one object is performed byassociating the object with one of the mentioned classes independentlyof the sensor data and thus solely based on the image. To this, aclassification device or a classifier is used, which applies apredetermined classification model. The classification result of thefirst classification is then compared to the classification result ofthe second classification, and the diagnosis is performed depending onthis comparison.

According to the invention, a diagnostic method is accordingly provided,in which a detected object is classified with respect to the sameclasses with two classification methods independent of each other. Forclassifying the detected object, sensor data of a vehicle sensor istaken into account on the one hand, which describes the environmentalconditions and thus the scene at the time of capture of the image. Thisfirst classification can be performed by means of a splitter withoutmuch effort and is particularly little prone to error, since sensor datais used for determining the environmental conditions and theclassification thus can be effected directly depending on the measured,known environmental conditions. On the other hand, the object isclassified with the aid of a predetermined classification model orclassification algorithm (classifier), wherein only the image or thedetected object is taken as a basis for this second classification,without using the sensor data to this. If a deviation between the twoclassification results is determined, thus, this presents an indicationof a possible error of the image processing device and/or theclassification device. With the method according to the invention, sucherrors can be diagnosed without much effort and particularly reliably,and in case of error, a warning message can be output to the driver. Anerroneous operation of the camera system can thus be prevented.

Preferably, the camera is a front camera, which is in particulardisposed behind a windshield of the motor vehicle, for example directlyon the windshield in the interior of the motor vehicle. Then, the frontcamera captures the environment in direction of travel or in vehiclelongitudinal direction in front of the motor vehicle. This can inparticular imply that a camera axis extending perpendicularly to theplane of the image sensor is oriented parallel to the vehiclelongitudinal axis.

Preferably, the camera is a video camera, which is able to provide aplurality of images (frames) per second. The camera can be a CCD cameraor a CMOS camera.

Thus, in an embodiment it can be provided that, if it is detected by thecamera system that the object was associated with a different class bythe first classification than by the second classification, an errorsignal is generated by the camera system. This error signal can forexample generate a warning message in an instrument cluster of the motorvehicle. Additionally or alternatively, such an error signal can alsoresult in the camera system being deactivated and thus put out ofservice in order to avoid erroneous detections.

The detected object is classified with respect to several predeterminedclasses, wherein the classes differ from each other in the environmentalconditions of the motor vehicle. Therein, the object is associated witha class among the several classes. In particular, in this context, itcan be provided that the several classes differ from each other in abrightness of the environmental region. A class can for example includeobjects, which are detected in a bright environment (in the daytime).Another class can for example include objects, which are detected in adark environment (at night). Several degrees of brightness can also bedefined.

Additionally or alternatively, the classes can also differ from eachother in atmospheric conditions of the environmental region and thus inweather conditions. Therein, one of the classes can for exampleassociated with rain, while another class can for example includeobjects, which are detected in dry environment. Here too, multipleintermediate stages can be defined.

If the above mentioned embodiments are combined with each other, thus, afirst class can for example include objects, which are detected in adark environment and in rain, while another class can include objects,which are detected in the daytime and in a dry environment.

It proves particularly advantageous if the following data is provided assensor data and taken into account in the first classification of theobject:

-   -   Data indicating a current operating state of a headlight of the        motor vehicle, and/or    -   data characterizing a current operating state of a windshield        wiper of the motor vehicle, and/or    -   sensor data of a rain sensor, and/or    -   sensor data of a brightness sensor of the motor vehicle.

The above mentioned data has the advantage that the brightness of theenvironment and/or the current weather conditions can be determinedbased on this data. Namely, this data allows conclusions to thebrightness of the environment and/or to the atmospheric conditions ofthe environmental region and thus allows reliable (first) classificationof the at least one object

With respect to the classification model, by means of which the secondclassification is performed, the following embodiments can be provided:

For example a HOG classifier (Histogram of Oriented Gradients) and/or anSVM classifier (Support Vector Machine) and/or a classifier based on aclassification tree method can be used as the classification model.These algorithms can be trained on the one hand and allow reliable andprecise classification of objects on the other hand.

In particular, an algorithm is used as the classification model, whichis trained with test image data with respect to the classification. Intraining the algorithm, preferably, a test database is provided, whichincludes test image data, that is a very great number of images. Thistest image data include a plurality of objects, which are detected bymeans of a corresponding object detection algorithm and then divided inclasses. These classified objects are then “communicated” to thealgorithm and thus taken as a basis for the training of the algorithm.Thus, these classes are to be “learned” by the algorithm. An alreadytrained algorithm is then capable of classifying the detected objectswith respect to the learned classes.

Preferably, the training of the algorithm is effected as follows: First,objects are detected in the test image data. Furthermore, the classesare defined. As already explained, the classes differ from each other inthe environmental conditions and thus in the scene at the time ofcapture of the image data. Sensor data is associated with the test imagedata, which is provided by means of at least one sensor and whichcharacterizes the environmental conditions at the time of capture of thetest image data. The detected objects are each associated with a classamong the mentioned classes depending on this sensor data. Theseclassified objects are then taken as a basis for training the algorithm.By the use of sensor data of at least one vehicle sensor, the trainingof the algorithm can be particularly precisely and reliably performed.Namely, the training is based on already classified objects, which havebeen very precisely classified and thus associated with the respectiveclasses based on the sensor data. Thus, the training can beautomatically performed, whereby a plurality of test images can also beused for this training and thus the accuracy and reliability of thetrained algorithm can also be improved.

With respect to the time of training, fundamentally, two embodiments canbe provided:

The training of the algorithm can be performed in a development phase ofthe camera system. In this embodiment, the test image data can forexample be provided by means of a camera, which is attached to a testvehicle. The sensor data is also provided by means of at least onesensor of this test vehicle at the same time and temporally associatedwith the test image data.

Additionally or alternatively, the training of the algorithm can also beperformed in the operation of the camera system based on image data,which is provided in the operation of the camera system by means of thecamera. This approach has the advantage that the algorithm can thus berendered more precise and thus improved also during the operation,thereby further reducing the probability of an error.

In addition, the invention relates to a camera system for a motorvehicle, wherein the camera system is formed for performing a methodaccording to the invention.

A motor vehicle according to the invention, in particular passenger car,includes a camera system according to the invention.

The preferred embodiments presented with respect to the method accordingto the invention and the advantages thereof correspondingly apply to thecamera system according to the invention as well as to the motor vehicleaccording to the invention.

Further features of the invention are apparent from the claims, thefigures and the description of figures. All of the features and featurecombinations mentioned above in the description as well as the featuresand feature combinations mentioned below in the description of figuresand/or shown in the figures alone are usable not only in therespectively specified combination, but also in other combinations orelse alone.

Below, the invention is explained in more detail based on a preferredembodiment as well as with reference to the attached drawings.

There show:

FIG. 1 in schematic illustration a motor vehicle with a camera systemaccording to an embodiment of the invention; and

FIGS. 2 to 4 block diagrams for explaining a method according to anembodiment of the invention.

A motor vehicle 1 shown in FIG. 1 is a passenger car in the embodiment.The motor vehicle 1 includes a camera system 2 serving for example as acollision warning system, by means of which the driver of the motorvehicle 1 can be warned of a risk of collision. Additionally oralternatively, the camera system 2 can be formed as an automatic brakeassist system, by means of which the motor vehicle 1 can beautomatically decelerated due to a detected risk of collision.

The camera system 2 includes a camera 3, which is formed as a frontcamera. The camera 3 is disposed in the interior of the motor vehicle 1on a windshield of the motor vehicle 1 and captures an environmentalregion 4 in front of the motor vehicle 1. The camera 3 is for example aCCD camera or else a CMOS camera. The camera 3 is additionally a videocamera providing a sequence of images of the environmental region 4 andcommunicating it to an image processing device not illustrated in thefigures. This image processing device and the camera 3 can optionallyalso be integrated in a common housing.

As is apparent from FIG. 1, an object 6, here a target vehicle, islocated on a roadway 5 in front of the motor vehicle 1. The imageprocessing device is set up such that it can apply a detection algorithmto the images of the environmental region 4, which is adapted fordetecting objects 6. This detection algorithm can for example be storedin a memory of the image processing device and for example be based onthe algorithm AdaBoost. Such detection algorithms are already prior artand are not described in more detail here. If the object 6 is detected,thus, it can be tracked over time by the image processing device. Tothis too, corresponding tracking algorithms are known.

The camera system 2 is formed such that it can perform a diagnosis ofthe camera system 2 in the operation. To this, a classification deviceis employed, which is able to classify the detected objects 6 using apredetermined classification model. This classification model representsan algorithm, which is first trained in an “offline” phase (in thedevelopment of the camera system 2) and/or in the operation of the motorvehicle 1. For example a HOG classifier and/or an SVM classifier and/ora classification tree method can be used as the algorithm.

A block diagram serving for training the algorithm or the classificationmodel is schematically illustrated in FIG. 2. At least one vehiclesensor 7 (a sensor of a test vehicle and/or a sensor of the motorvehicle 1) provides sensor data SD characterizing the environmentalconditions of the motor vehicle. For example, the following sensor dataSD is provided:

-   -   data indicating a current operating state of a headlight of the        motor vehicle 1 (or of the test vehicle), and/or    -   data indicating a current operating state of a windshield wiper        of the motor vehicle 1 (or of the test vehicle), and/or    -   sensor data of a rain sensor and/or of a brightness sensor of        the motor vehicle 1 (or of the test vehicle).

By means of the camera 3 (it can be attached to the test vehicle and/orto the motor vehicle 1 in FIG. 2), test image data TBD is provided. Boththe sensor data SD and the test image data TBD are supplied to an objectdetection algorithm 8, which can be implemented in the mentioned imageprocessing device. The object detection algorithm 8 detects an object 6in the image data TBD and delivers it to a splitter 9, whichadditionally also receives the sensor data SD. The splitter 9 associatesthe detected object 6 with a class among several predetermined classesK1 to K4 depending on the sensor data SD. This means that the object 6is classified depending on the environmental conditions and is hereinassociated with one of the classes K1 to K4.

The classes K1 to K4 can differ from each other in the brightness of theenvironment and/or in the atmospheric conditions. One of the classes K1to K4 can be intended for objects 6, which are detected in a darkenvironment and in rain. Another one of the classes K1 to K4 can beintended for objects 6, which are detected in a bright and dryenvironment. With which one of the classes K1 to K4 the detected object6 is associated, is determined depending on the sensor data SD.

Basically, the number of the classes K1 to K4 is not restricted.However, the number of the classes K1 to K4 is determined such that eachof the classes K1 to K4 contains a minimum number of objects in trainingthe algorithm.

The classified objects are then supplied to a learner 10 in order tothus generate and train the classification model 11, respectively. Theclassification model 11 thus represents a classification algorithm andtherefore a classifier.

Thus, the above described method serves for training the classificationmodel 11 with the aid of test image data TBD. As already explained, thetraining can be performed in the development of the camera system 2and/or in the operation of the motor vehicle 1. Therein, a test vehiclecan be used in the development, to which a corresponding camera 3 isattached, which provides the test image data. At least one sensor 7 ofthis test vehicle then provides the sensor data SD. If the training isperformed in the operation of the motor vehicle 1, thus, the test imagedata is provided by the camera 3 of the motor vehicle 1, while thesensor data SD is provided by means of at least one vehicle sensor 7 ofthe motor vehicle 1.

In the operation of the camera system 2, then, a diagnosis is performed.A corresponding block diagram is illustrated in FIG. 3: at least onevehicle sensor 7 of the motor vehicle 1 provides current sensor data SD,which can include the above already mentioned data, that is informationabout the current operating state of the headlight and/or informationabout the current operating state of the windshield wiper and/orinformation of a rain sensor and/or information of a brightness sensor.The camera 3 provides current image data BD. Both the sensor data SD andthe image data BD are supplied to the object detection algorithm 8,which is implemented in the mentioned image processing device. Theobject detection algorithm 8 detects an object 6 and communicates theresult of the detection to a classification device 12 on the one hand,in which the trained classification algorithm or the classificationmodel 11 is implemented, as well as to the splitter 9 on the other hand.The splitter 9 and/or the classification device 12 can also beimplemented in the image processing device.

The splitter 9 additionally receives the current sensor data SD.However, this sensor data SD is not communicated to the classificationdevice 12.

Now, the detected object 6 is classified in two different manners,namely by means of the splitter 9 on the one hand and also by means ofthe classification model 11 on the other hand. The splitter 9 performs afirst classification of the object 6 by associating the object 6 withone of the classes K1 to K4 depending on the sensor data SD.Independently of this and thus independently of the sensor data SD, theobject 6 is also classified by means of the classification model 11,which associates the object 6 with one of the classes K1 to K4.

A diagnostic device 13, which can also be implemented in the imageprocessing device, then compares the classification results of the firstand the second classification and performs the diagnosis of the camerasystem 2 depending on the comparison. If a deviation between theclassification results is determined, an error signal 14 can be output.

The camera system 2 according to FIG. 4 differs from the camera system 2according to FIG. 3 in that the classification device 12 with theclassification model 11 is a part of the object detection algorithm 8.In this case, the training of the classification model 11 constitutes apart of the system or of the object recognition algorithm 8. Here, thetraining can only include a corresponding setting of the splitter 9 inorder to obtain the same results as with the object detection algorithm8. An advantage of this embodiment is in the reduction of the computingtime by the use of results of the object recognition algorithm 8.

The invention claimed is:
 1. A method for performing a diagnosis of acamera system of a motor vehicle, comprising: providing at least oneimage of an environmental region of the motor vehicle by a camera of thecamera system; detecting at least one object external to vehicle in theat least one image by an image processing device of the camera system;providing sensor data by at least one sensor of the motor vehicle,wherein the sensor data characterizes environmental conditions of themotor vehicle; first classifying the at least one object and hereinassociating the object with a class among a plurality of predeterminedclasses depending on the environmental conditions, wherein the pluralityof classes differ from each other with respect to the environmentalconditions, second classifying the at least one object and hereinassociating the object with one of the plurality of classes based on theimage, and independently of the sensor data, by a classification deviceusing a predetermined classification model; and comparing classificationresults of the first and the second classification and performing adiagnosis depending on the comparison.
 2. The method according to claim1, further comprising: generating an error signal by the camera system,when the camera system detects that the object has been associated witha different class by the first classification than by the secondclassification.
 3. The method according to claim 1, wherein theplurality of classes differ from each other in a brightness of theenvironmental region.
 4. The method according to claim 1, wherein theplurality of classes differ from each other in atmospheric conditions ofthe environmental region.
 5. The method according to claim 1, whereinthe following data is provided as the sensor data: data indicating acurrent operating state of a headlight of the motor vehicle, and dataindicating a current operating state of a windshield wiper of the motorvehicle, and sensor data of a rain sensor and/or of a brightness sensorof the motor vehicle.
 6. The method according to claim 1, wherein a HOGclassifier and/or an SVM classifier and/or a classifier based on aclassification tree method is used as the classification model.
 7. Themethod according to claim 1, wherein an algorithm is used as theclassification model, which is trained with test image data with respectto the classification.
 8. The method according to claim 7, wherein thetraining of the algorithm includes that objects are detected in the testimage data and are each associated with one of the plurality of classesdepending on sensor data of at least one sensor, which characterizesenvironmental conditions at the time of capture of the test image data,and wherein the classified objects are taken as a basis for training thealgorithm.
 9. The method according to claim 7, wherein the training ofthe algorithm is performed in a development phase of the camera system.10. The method according to claim 7, wherein the training of thealgorithm is performed in the operation of the camera system based onimage data, which is provided in the operation of the camera system bythe camera.
 11. A camera system for a motor vehicle, wherein the camerasystem is adapted to perform a method according to claim
 1. 12. A motorvehicle passenger car comprising a camera system according to claim 11.